Graph-Native Infrastructure for Context and Accountable AI Systems
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Updated
Oct 1, 2026 - Python
Graph-Native Infrastructure for Context and Accountable AI Systems
A graph-native memory system for AI agents and context graphs. Store conversations, build knowledge graphs, and let your agents learn from their own reasoning — all backed by Neo4j.
Transforms Databricks Unity Catalog tables into a materialized knowledge graph, with ontology design and reasoning exposed as tools via MCP.
world-model-mcp is a signed audit memory server for AI coding agents, delivered as an MCP tool. Every event Ed25519-signed and Merkle-chained; offline-verifiable against pinned public keys. Hosted platform with hybrid post-quantum signing at etch.systems.
Recursive learning framework, give any AI agent a self-improvement loop with memory. No fine-tuning, just API calls
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